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Reliability is an important factor in route, mode and also departure time choice analysis and is a key performance indicator for transport systems. However, the current metrics used to measure travel time variability may be not sufficient to fully represent reliability. Better understanding of the distributions of travel times is needed for the development of improved metrics for reliability. A comprehensive data analysis involving the assessment of longitudinal travel time data for two urban arterial road corridors in Adelaide, Australia, demonstrates that the observed distributions are more complex than previously assumed. The data sets demonstrate strong positive skew, very long upper tails, and sometimes bimodality. This paper proposes the use of alternative statistical distributions for travel time variability, with the Burr Type XII distribution emerging as an appropriate model for both links and routes. This statistical distribution has some attractive properties that make it suitable for explicit definition of many travel time reliability metrics. This is in keeping with the broad planning definition of travel time reliability as the level of variation from day to day for a trip made by an individual starting at about the same time each day (e.g. see [22]). a Fitting normal distribution to the link travel times. b Fitting normal distribution to the logarithmic values of the link travel times. 725 TRAVEL TIME VARIABILITY ON URBAN ROADS Figure 7. Burr distribution and observed cumulative density functions for the South Road route travel times. 731 TRAVEL TIME VARIABILITY ON URBAN ROADS

Background: Many tuberculosis (TB) patients in Indonesia are diagnosed late. We seek to document patient journeys toward TB diagnosis and treatment and factors that influence health care seeking behavior.

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